Improving the precipitation forecasts of the North-American multi model ensemble (NMME) over Sistan basin

• Published in: Journal of hydrology (Amsterdam) Vol. 590; p. 125263
• Main Authors: Yazdandoost, Farhad, Moradian, Sogol, Zakipour, Mina, Izadi, Ardalan, Bavandpour, Majid
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-11-2020
• Subjects: Bias correction; Copula method; Hybrid method; NMME; Precipitation forecasts; Bias correction; Hybrid method; NMME; Copula method; Precipitation forecasts
• ISSN: 0022-1694; 1879-2707
• DOI: 10.1016/j.jhydrol.2020.125263

•Precipitation forecasts of four NMME models were compared over Sistan Basin.•Seasonal and subseasonal precipitation predictions were improved.•Statistical-dynamical improvement methods were applied: The GrandNMME, QM, Copula.•A novel hybrid approach for ensemble weighting was developed.•The proposed techniques provide more accurate forecasts.
Poor forecasting of climate variables at river basin scale, leads to situations which entail considerable risks and losses in various sectors such as the agriculture, the environment and water resources. Lack of accurate sub-seasonal and seasonal precipitation predictions are major management and operation predicaments in the transboundary Sistan Basin, located in Afghanistan and Iran, and selected as the case study in this paper. The outputs of theNorth-American Multi Model Ensemble (NMME), as a seasonalforecasting system, were utilized in the target region. In order to evaluate the performance of the raw outputs of four NMME models (namely: NCEP-CFSv2, CMC-CanCM3, CMC2-CanCM4, NCAR-CCSM4), the individual model ensemble mean was compared to the observations. Results highlighted the need for post-processing of the NMME outputs. For this purpose, three different commonly used methods, including GrandNMME, bias correction quantile mapping (QM) and Copula approaches, alongside a novel hybrid technique, were applied to improve future predictions of precipitation patterns for the hindcast period of 1982–2010 and forecast period of 2012–2016. All four methods were performed for each month in each 0.5-degree cell over the following one to six months. The findings demonstrated the performance of different NMME models were not the same over time and space. Among the models, the NCEP-CFSv2 and CMC2-CanCM4 seemed to best capture the spatial variability of precipitation in the study area. In addition, they performed better in estimating the amounts of precipitation. Among the methods, the QM, the Copula and the hybrid were all successful in improving the geographic patterns of the data. Furthermore, they were all able to enhance the accuracy of the data. At different timescales, the hybrid method showed substantial improvements over the seasonal predictions. Overall, the findings illustrated that collective use of methods may narrow the potential uncertainty in projections of regional changes.